PCA analysis
Goal: Do a PCA plot using Irene’s SNPs to look for relatedness among plants. Also figure out loading (which genes contribute most)
Snp calling with freebayes
First, need to call SNPs using all BAM files at once:
working in directory 2019/IreneSnps on whitney
first, sort bams
for f in SNPanalysis/*LT*rmdup.bam
do
newname=`basename $f .bam`_sort.bam
samtools sort -o $newname --reference SNPanalysis/Pinsaporeference1 $f
donenext, assign read groups to bams
input=""
for f in `ls *sort.bam`
do
rg=`basename $f _rmdup_sort.bam`
input="$input -b $f -r $rg -s $rg"
done
echo $input
bamaddrg $input > LT_rmdup_sort_combined.bam
samtools index LT_rmdup_sort_combined.bamfreebayes -f SNPanalysis/Pinsaporeference1 --no-indels --no-mnps --no-complex LT_rmdup_sort_combined.bam > LT.vcf &try parallel
ulimit -n 4000
/usr/local/stow/freebayes/scripts/fasta_generate_regions.py Pinsaporeference1.fai 100000 > regions
./freebayes-parallel regions 8 -f Pinsaporeference1 --no-indels --no-mnps --no-complex LT_rmdup_sort_combined.bam > LT.vcf (note: I edited the freebayes-parallel script so that it would work…)
Freeybayes parallel takes about 12 hours
scp whitney.plb.ucdavis.edu:2019/IreneSnps/LT.vcf.gz ../input/now analyze vcf in R
library(tidyverse)[30m── [1mAttaching packages[22m ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──[39m
[30m[32m✔[30m [34mggplot2[30m 3.0.0 [32m✔[30m [34mpurrr [30m 0.2.5
[32m✔[30m [34mtibble [30m 1.4.2 [32m✔[30m [34mdplyr [30m 0.7.6
[32m✔[30m [34mtidyr [30m 0.8.1 [32m✔[30m [34mstringr[30m 1.3.1
[32m✔[30m [34mreadr [30m 1.1.1 [32m✔[30m [34mforcats[30m 0.3.0[39m
[30m── [1mConflicts[22m ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
[31m✖[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter()
[31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39mlibrary(ggrepel)get the vcf header
vcf.header <- system("zgrep '#C' ../input/LT.vcf.gz",intern = TRUE)
vcf.header[1] "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t38LTR\t42LTR\t42LTRR\t43LTR\t43LTRR\t49LTWR\t49LTWRR\t95LTWR\t95LTWRR\t99LTWR"vcf.header <- vcf.header %>%
str_replace("#","") %>% #get rid of the pound sign
str_split(pattern = "\t") %>% #split on the tabs
magrittr::extract2(1)
vcf.header [1] "CHROM" "POS" "ID" "REF" "ALT" "QUAL" "FILTER" "INFO" "FORMAT" "38LTR" "42LTR" "42LTRR" "43LTR"
[14] "43LTRR" "49LTWR" "49LTWRR" "95LTWR" "95LTWRR" "99LTWR" get the data
snps <- read_tsv("../input/LT.vcf.gz", na = c("","NA","."),comment="#",col_names = vcf.header) %>%
select(-ID, -FILTER) # these are empty columnsParsed with column specification:
cols(
CHROM = col_character(),
POS = col_integer(),
ID = col_character(),
REF = col_character(),
ALT = col_character(),
QUAL = col_double(),
FILTER = col_character(),
INFO = col_character(),
FORMAT = col_character(),
`38LTR` = col_character(),
`42LTR` = col_character(),
`42LTRR` = col_character(),
`43LTR` = col_character(),
`43LTRR` = col_character(),
`49LTWR` = col_character(),
`49LTWRR` = col_character(),
`95LTWR` = col_character(),
`95LTWRR` = col_character(),
`99LTWR` = col_character()
)
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|========================================================================================================================| 100% 398 MBsnpsfilter to keep snps where there is data from all samples
snps <- snps %>%
filter({select(., matches("[0-9]")) %>% complete.cases() })
snpssnps <- snps %>%
mutate(TOTAL_DEPTH= {str_extract(INFO, "DP=[0-9]*") %>%
str_remove("DP=") %>%
as.numeric() }
) %>%
filter(QUAL >=100,
nchar(ALT)==1,
TOTAL_DEPTH > quantile(TOTAL_DEPTH, 0.05),
TOTAL_DEPTH < quantile(TOTAL_DEPTH, 0.95))
snpsunpack the information differnet samples:
samples <- colnames(snps) %>% str_subset("^[0-9]")
for (s in samples) {
snps <- snps %>%
separate(!!s, into=paste(s,c("gt","tot.depth","allele.depth","ref.depth","ref.qual","alt.depth","alt.qual","gt.lik"),sep="_"),
sep=":", convert = TRUE)
}
snpsFor the PCA we only need the genotype info
gts <- snps %>%
select(CHROM, POS, ends_with("_gt"))
gtsremove the 38LTR gample
gts <- gts %>% select(-`38LTR_gt`)convert this to numeric
geno.numeric <- gts %>%
select(-CHROM, -POS) %>%
lapply(factor) %>% # convert charcters to "factors", where each category is internally represented as a number
as.data.frame() %>% # reformat
data.matrix() %>%# convert to numeric
t()
colnames(geno.numeric) <- str_c(gts$CHROM, "_", gts$POS)
head(geno.numeric[,1:5],10) GCZN01000007.1_2815 GCZN01000007.1_2834 GCZN01000007.1_2865 GCZN01000007.1_2881 GCZN01000007.1_2970
X42LTR_gt 2 3 2 2 2
X42LTRR_gt 2 2 2 2 1
X43LTR_gt 2 2 2 2 2
X43LTRR_gt 2 2 2 2 2
X49LTWR_gt 2 2 2 2 2
X49LTWRR_gt 3 3 3 3 1
X95LTWR_gt 2 3 3 2 2
X95LTWRR_gt 2 3 3 2 2
X99LTWR_gt 2 2 2 2 2dim(geno.numeric)[1] 9 134421dim(gts)[1] 134421 11get the principal components
pca <- prcomp(geno.numeric)
summary(pca)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9
Standard deviation 78.9711 75.4129 60.8894 54.9581 52.3780 45.30719 43.88667 42.97555 1.288e-11
Proportion of Variance 0.2291 0.2089 0.1362 0.1110 0.1008 0.07541 0.07076 0.06785 0.000e+00
Cumulative Proportion 0.2291 0.4380 0.5742 0.6852 0.7860 0.86139 0.93215 1.00000 1.000e+00plot it
plot.data <- pca$x %>%
as.data.frame %>%
rownames_to_column("sample") %>%
mutate(response=str_extract(sample, "(LTR|LTWR)")) %>%
gather(key="component", value="value",PC2:PC9)
plots <- map(sort(unique(plot.data$component)), function(x) {
plot.data %>%
filter(component==x) %>%
ggplot(aes(x=PC1, y= value, label=sample, color=response)) +
geom_point() + ylab(x)
}
)
plots [[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
snps that contribute the most to PC1:
loadings <- as.data.frame(pca$rotation) %>%
rownames_to_column("snp") %>%
select(PC1, snp) %>%
arrange(desc(abs(PC1)))
loadingscontributions <- loadings %>%
separate(snp, into=c("contig", "pos"), sep="_") %>%
group_by(contig) %>%
summarize(abs.contribution = abs(sum(PC1)),
contribution = sum(PC1),
number_of_snps = n()) %>%
arrange(desc(abs.contribution))
contributionsbring in seq lineghts
lengths <- read_csv("../input/Pinsaporeference1_lengths.csv", col_names = c("contig", "length"), skip=1) %>%
mutate(contig = str_remove(contig, " .*"))Parsed with column specification:
cols(
contig = col_character(),
length = col_integer()
)lengthscontributions <- contributions %>%
left_join(lengths) %>%
mutate(snps_per_100bp = round(number_of_snps / length * 100, 2)) %>%
select(contig, contribution, length, number_of_snps, snps_per_100bp)Joining, by = "contig"contributionswrite_csv(contributions, "../output/gene_contributions.csv.gz")make plot of PC1 vs PC2 for paper
pc1pc2 <- pca$x %>%
as.data.frame() %>%
rownames_to_column("ID") %>%
select(ID, PC1, PC2) %>%
mutate(ID={str_replace(ID, "W", "N") %>%
str_replace("RR", "R2") %>%
str_remove_all("(X|_gt)") },
response=ifelse(str_detect(ID,"N"), "no recovery", "recovery"))
pc1pc2pc1pc2 %>%
ggplot(aes(x=PC1, y = PC2, label=ID, color=response)) +
geom_point() +
geom_text_repel(show.legend=FALSE, direction="y")
ggsave("../output/PCA.pdf")Saving 7.29 x 4.51 in image
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IHsKICBwbG90LmRhdGEgJT4lCiAgICBmaWx0ZXIoY29tcG9uZW50PT14KSAlPiUKICAgIGdncGxvdChhZXMoeD1QQzEsIHk9IHZhbHVlLCBsYWJlbD1zYW1wbGUsIGNvbG9yPXJlc3BvbnNlKSkgKwogICAgICBnZW9tX3BvaW50KCkgKyB5bGFiKHgpCiAgfQogICkKCnBsb3RzICAKYGBgCgojIyBzbnBzIHRoYXQgY29udHJpYnV0ZSB0aGUgbW9zdCB0byBQQzE6CgpgYGB7cn0KbG9hZGluZ3MgPC0gYXMuZGF0YS5mcmFtZShwY2Ekcm90YXRpb24pICU+JQogIHJvd25hbWVzX3RvX2NvbHVtbigic25wIikgJT4lCiAgc2VsZWN0KFBDMSwgc25wKSAlPiUKICBhcnJhbmdlKGRlc2MoYWJzKFBDMSkpKQpsb2FkaW5ncwpgYGAKCmBgYHtyfQpjb250cmlidXRpb25zIDwtIGxvYWRpbmdzICU+JQogIHNlcGFyYXRlKHNucCwgaW50bz1jKCJjb250aWciLCAicG9zIiksIHNlcD0iXyIpICU+JQogIGdyb3VwX2J5KGNvbnRpZykgJT4lCiAgc3VtbWFyaXplKGFicy5jb250cmlidXRpb24gPSBhYnMoc3VtKFBDMSkpLAogICAgICAgICAgICBjb250cmlidXRpb24gPSBzdW0oUEMxKSwKICAgICAgICAgICAgbnVtYmVyX29mX3NucHMgPSBuKCkpICU+JQogIGFycmFuZ2UoZGVzYyhhYnMuY29udHJpYnV0aW9uKSkKY29udHJpYnV0aW9ucwpgYGAKCmJyaW5nIGluIHNlcSBsaW5lZ2h0cwoKYGBge3J9Cmxlbmd0aHMgPC0gcmVhZF9jc3YoIi4uL2lucHV0L1BpbnNhcG9yZWZlcmVuY2UxX2xlbmd0aHMuY3N2IiwgY29sX25hbWVzID0gYygiY29udGlnIiwgImxlbmd0aCIpLCBza2lwPTEpICU+JQogIG11dGF0ZShjb250aWcgPSBzdHJfcmVtb3ZlKGNvbnRpZywgIiAuKiIpKQpsZW5ndGhzCmBgYAoKYGBge3J9CmNvbnRyaWJ1dGlvbnMgPC0gY29udHJpYnV0aW9ucyAlPiUgCiAgbGVmdF9qb2luKGxlbmd0aHMpICU+JQogIG11dGF0ZShzbnBzX3Blcl8xMDBicCA9IHJvdW5kKG51bWJlcl9vZl9zbnBzIC8gbGVuZ3RoICogMTAwLCAyKSkgJT4lCiAgc2VsZWN0KGNvbnRpZywgY29udHJpYnV0aW9uLCBsZW5ndGgsIG51bWJlcl9vZl9zbnBzLCBzbnBzX3Blcl8xMDBicCkKY29udHJpYnV0aW9ucwpgYGAKCmBgYHtyfQp3cml0ZV9jc3YoY29udHJpYnV0aW9ucywgIi4uL291dHB1dC9nZW5lX2NvbnRyaWJ1dGlvbnMuY3N2Lmd6IikKYGBgCgojIyBtYWtlIHBsb3Qgb2YgUEMxIHZzIFBDMiBmb3IgcGFwZXIKCmBgYHtyfQpwYzFwYzIgPC0gcGNhJHggJT4lCiAgYXMuZGF0YS5mcmFtZSgpICU+JQogIHJvd25hbWVzX3RvX2NvbHVtbigiSUQiKSAlPiUKICBzZWxlY3QoSUQsIFBDMSwgUEMyKSAlPiUKICBtdXRhdGUoSUQ9e3N0cl9yZXBsYWNlKElELCAiVyIsICJOIikgJT4lCiAgICAgICBzdHJfcmVwbGFjZSgiUlIiLCAiUjIiKSAlPiUKICAgICAgc3RyX3JlbW92ZV9hbGwoIihYfF9ndCkiKSB9LAogICAgcmVzcG9uc2U9aWZlbHNlKHN0cl9kZXRlY3QoSUQsIk4iKSwgIm5vIHJlY292ZXJ5IiwgInJlY292ZXJ5IikpCnBjMXBjMgpgYGAKCmBgYHtyfQpwYzFwYzIgJT4lCiAgZ2dwbG90KGFlcyh4PVBDMSwgeSA9IFBDMiwgbGFiZWw9SUQsIGNvbG9yPXJlc3BvbnNlKSkgKwogIGdlb21fcG9pbnQoKSArCiAgZ2VvbV90ZXh0X3JlcGVsKHNob3cubGVnZW5kPUZBTFNFLCBkaXJlY3Rpb249InkiKQpnZ3NhdmUoIi4uL291dHB1dC9QQ0EucGRmIikKYGBg